Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be “vital” to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary’s karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

Studies that measure individual differences leading to risky driving behaviors in pre-crash phase will make contributions to accidents prevention. The primary concern here is what induces drivers to engage in risky behaviors. In this research, 441 valid questionnaires were distributed to examine the impact of risk preference, risk perception, and their interaction on risky driving behaviors, and the moderating effects of gender, age, and driving experience were measured accordingly. Results from the ordered logit regression model analysis demonstrate that the data fit well with our theoretical model. Risk preference and risk perception both predict risky driving behaviors with risk perception having greater predictability, and their interaction significantly affects risky driving behaviors when gender and age variables were added to the model separately. Gender and driving experience moderate the influence of risk perception on risky driving behaviors. The predictive effect of risk perception on risky driving behaviors was more significant for females than males. The effect of risk perception on risky driving behaviors was more pronounced for drivers with 1-3 years of driving experience compared with others. These interesting findings suggest that interventions need to be directed to all parts of the causal chain.

Measuring centrality has recently attracted increasing attention, with algorithms ranging from those that simply calculate the number of immediate neighbors and the shortest paths to those that are complicated iterative refinement processes and objective dynamical approaches. Indeed, vital nodes identification allows us to understand the roles that different nodes play in the structure of a network. However, quantifying centrality in complex networks with various topological structures is not an easy task. In this paper, we introduce a novel definition of entropy-based centrality, which can be applicable to weighted directed networks. By design, the total power of a node is divided into two parts, including its local power and its indirect power. The local power can be obtained by integrating the structural entropy, which reveals the communication activity and popularity of each node, and the interaction frequency entropy, which indicates its accessibility. In addition, the process of influence propagation can be captured by the two-hop subnetworks, resulting in the indirect power. In order to evaluate the performance of the entropy-based centrality, we use four weighted real-world networks with various instance sizes, degree distributions, and densities. Correspondingly, these networks are adolescent health, Bible, United States (US) airports, and Hep-th, respectively. Extensive analytical results demonstrate that the entropy-based centrality outperforms degree centrality, betweenness centrality, closeness centrality, and the Eigenvector centrality.